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The emergence of building performance optimization is recognized as a way to achieve sustainable building designs. In this paper, the problem consists in minimizing simultaneously the emissions of greenhouse gases (GHG) related to building energy consumption and those related to building materials. This multi-objective optimization problem involves variables with different hierarchical levels, i.e. variables that can become obsolete depending on the value of the other variables. To solve it, NSGA-II is compared with an algorithm designed specifically to deal with hierarchical variables, namely sNSGA. Evaluation metrics such as convergence, diversity and hypervolume show that both algorithms handle hierarchical variables, but the analysis of the Pareto front confirms that in the present case, NSGA-II is better to identify optimal solutions than sNSGA. All the optimal solutions are made of buildings with wooden envelopes and relied either on heat pumps or on electrical heaters for proving heating.

This paper describes an improved measurement method and sensor development for the assessment of the indoor thermal environment. The disadvantage of the state of the art concept of the equivalent temperature to describe inhomogeneous environments is pointed out and how the DressMAN 3.2 sensor system provides improvements to this measurement principle. The measurement system has been prepared in order to accompany thermal tests in the Passenger Cabin Ground Demonstrator developed within the CleanSky2 Regional project. This shall allow the objective assessment of the passenger comfort in the mock-up without the need for extensive subject test campaigns.